Anisotropic Feature-Preserving Denoising of Height Fields and Bivariate Data.

TLDR: An efficient way to denoise bivariate data like height fields, color pictures or vector fields, while preserving edges and other features is presented, which generalizes previous anisotropic diffusion approaches in image processing, and is applicable to data of arbitrary dimension.

Abstract: In this paper, we present an efficient way to denoise bivariate data like height fields, color pictures or vector fields, while preserving edges and other features. Mixing surface area minimization, graph flow, and nonlinear edge-preservation metrics, our method generalizes previous anisotropic diffusion approaches in image processing, and is applicable to data of arbitrary dimension. Another notable difference is the use of a more robust discrete differential operator, which captures the fundamental surface properties. We demonstrate the method on range images and height fields, as well as greyscale or color images. CR Categories: I.3.7 [Computer Graphics] Three-Dimensional Graphics and Realism; I.4.3 [Image Processing and Computer Vision] Enhancement.